Takeaways
- Data availability is catching up with document complexity.
- Intelligent Document Processing (IDP) uses machine learning and natural language processing to catch what ETL and ELT can’t.
- IDP extracts data from documents at ingestion points and outputs structured data in formats like JSON, CSV, and XML.
- The IDP market is accelerating as it improves data quality and expands data possibilities in lakehouses.
Historically, traditional methods of capturing document data have fallen short of large-scale analytics—mostly offering indexing and basic metadata. Variations in document structure and schema have always posed a challenge for capture solutions.
[Intelligent Document Processing (IDP)][1] is now the second hottest tool on every analyst and CEO’s list (right after AI). As of 2025, 63% of Fortune 250 companies have implemented IDP to add structure to locked document data, vastly improving access for analytics and AI. Industry stats suggest an 80–90% increase in access to data when analyzing content once confined within documents[^2].
Let’s break down the why, how, and where IDP fits in a document-driven data pipeline.
Comparing Modern Data Querying Pipelines
Schema-on-Write (ETL: Extract, Transform, Load)
- Takes raw structured data (typically RDBMS, logs, APIs)
- Normalizes and structures with a predefined schema
- Loads into a Data Warehouse
Benefits: High performance/consistency, fast business reporting, reliable data quality, easy querying.
Schema-on-Read (ELT: Extract, Load, Transform)
- Extracts raw data
- Loads into a Data Lake
- Adds structure during queries, batch jobs, or scheduled tasks
Benefits: Handles all varieties (tables, logs, unstructured content). Schema is applied at query time.
The Data Lakehouse
Cloud architecture in platforms like Databricks and Snowflake blends data warehouse management with data lake flexibility. Today, [85% of organizations—a 20% increase from last year—leverage Data Lakehouse architecture][2] to store enterprise data for analytics and AI/ML projects. (And AI needs lots of data!)
Where Does the Lakehouse Struggle?
With so much data in the lakehouse, organizations either:
- Lack enough unstructured document data (that’s ~80% of enterprise data), or
- Have unformatted document data tossed in the lake—like sunken treasure
A lake without structure becomes a “data swamp”: a poorly managed, unusable repository of raw data.
Many lakehouses feature some native toolsets for document types, and custom Python can convert regular schemas to JSON, CSV, XML formats.
But with inconsistent or shifting formats, manual scripting error rates spike.
Parsing Unstructured Data to JSON
IDP uses NLP to make sense of documents with variable structures, extracting context and insights for analytics.
After capture, IDP delivers results as JSON, XML, CSV—no manual scripting or debugging needed.
Just well-trained AI/ML models, adapting instantly to new formats, providing digestible data as documents flow into your business.
Why does this matter?
Whether your data is a highly structured invoice or a rambling CEO letter, crucial data is littered everywhere.
IDP makes it analyzable—fueling decisions for data engineers and powerful prompts for LLMs.
The post “How IDP Boosts ELT & Lakehouse Analytics” was originally published on keymarkinc.com.
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